2025 PODS Awardees
The Michigan Institute for Data and AI in Society has announced the recipients of its 2025 Propelling Original Data Science (PODS) Grants. This year, MIDAS collaborated with Microsoft, the Michigan Institute for Healthcare Policy and Innovation, and the Michigan Institute for Clinical and Health Research to enhance the PODS program’s scope to include focused support for responsible AI endeavors, AI’s application in health policy activities, and AI advancements in clinical and translational sciences. Two project teams from the 2024 cycle were awarded continued funding to progress their efforts forward.
The 2025 PODS Grants are organized into several focused tracks:
- Track 1: Data Science and AI Methodology and Applications
- Track 2: Accelerating Responsible AI Research Ecosystems (Microsoft)
- Track 3A: AI Innovations in Clinical & Translational Sciences (CTS) (MIDAS + MICHR)
- Track 3B: AI impact and governance for health policy and healthcare (MIDAS + IHPI)
- Track 3C: Data science and AI for health science and healthcare research
Track 1: Data science and AI methodology and applications
- Harnessing AI for Advancing Data Collection and Population-Scale Causal Inference
William Axinn (Ford School of Public Policy), David Jurgens (School of Information), and James Wagner (Institute for Social Research) - The As-If Machine (AIM): A Multi-Agent RAG System for Reducing Psychological Distance Through Personalized Narrative Simulations
Ceren Budak (School of Information) and Stephanie Preston (College of Literature, Science, and the Arts) - SAGE: A Scalable GeoAI Framework for Zero-Shot Mapping of Lithium Mines
Joshua Newell (School for Environment and Sustainability) and Paramveer Dhillon (School of Information) - ML-Powered Anomaly Detection at the Speed of Light: Algorithms and Applications for Secure Power Grid Operations
Shubhanshu Shekhar (College of Engineering) and Vladimir Dvorkin (College of Engineering) - Harnessing Evolutionary Legacies in Protein Space: Evolvability as a new target for molecular optimization
Luis Zaman (College of Literature, Science, and the Arts), Robert Woods (Michigan Medicine), and Matthew O’Meara (Michigan Medicine)
Track 2: Accelerating responsible AI research ecosystems
- Continued Funding for 2024 PODS Awards: Evaluating Solutions to the Decline of Online Knowledge Communities
Yan Chen (School of Information) and Qiaozhu Mei (School of Information) - Governing AI’s Footprint: A Scalable Human-AI Workflow to Extract Zoning Codes for Data Centers and Renewable Energy Sitting
Xiaofan Liang (Taubman College of Architecture and Urban Planning) and Sarah Mills (Taubman College of Architecture and Urban Planning) - Facilitating Appropriate Reliance on Generative AI (GenAI) Tools by Investigating Reliance Decisions and Norms
Q. Vera Liao (College of Engineering) - AI Systems to Combat Non-Consensual Intimate Media (NCIM)
Sarita Schoenebeck (School of Information) and Eric Gilbert (School of Information) - Continued Funding for 2024 PODS Awards: A Joint Human-AI Framework for Responsible AI
Colleen Seifert (College of Literature, Science and the Arts), Rita Chin (College of Literature, Science and the Arts) and H.V. Jagadish (College of Engineering)
Track 3A: AI innovations in Clinical and Translational Sciences (CTS) (jointly funded by MICHR and MIDAS)
- Developing Best Practices for AI-assisted Mixed Methods Analysis
Timothy Guetterman (Michigan Medicine) and Melissa DeJonckheere (Michigan Medicine)
Track 3B: AI impact and governance for health policy and healthcare (jointly funded by IHPI and MIDAS)
- Implementing AI into Anticoagulation Clinical Decision Support
Geoffrey Barnes (Michigan Medicine), Michael Sjoding (Michigan Medicine), and Michael Lanham (Michigan Medicine) - Reducing Unplanned Hospital Readmissions with Causal Machine Learning
Jenna Wiens (College of Engineering) and Vikas Parekh (Michigan Medicine)
Track 3C: Data science and AI for health science and healthcare research
- Revolutionizing Disease Diagnostics Through the Integration of Physics-Informed Materials Science Methods with Sequence Models
Sharon Glotzer (College of Engineering) - Predictive Modeling and Feature Learning for Large-Scale Neuroimaging Data
Jian Kang (School of Public Health) and Chandra Sripada (Michigan Medicine, College of Literature, Science and the Arts) - Enhancing Drug Combination Therapies Through Heterophilic Link Prediction with Graph Neural Networks
Danai Koutra (College of Engineering) and Sriram Chandrasekaran (College of Engineering, Michigan Medicine)
2024 PODS Awardees
Track 1: Data science and AI methodology and applications
- WinAI: Propelling UM Soccer with Data-Driven AI
Albert Berahas (College of Engineering) and Raed Al Kontar (College of Engineering) - Human-in-the-loop multi-agent sequential decision-making based Optimal Operation of Power Distribution System
Srijita Das (College of Engineering and Computer Science, U-M Dearborn) and Van Hai Bui (College of Engineering and Computer Science, U-M Dearborn) - Extrapolating with Generative Models for Design of Organic Molecules as Energy Carriers
David Kwabi (College of Engineering), Bryan Goldsmith (College of Engineering), and Yixin Wang (College of Literature, Science and the Arts) - Multimodal Modeling of Cognitive Load at Individual and Team Levels in Acute Care Teams using VR Simulations
Vitaliy Popov (Michigan Medicine), Mohamed Abouelenien (College of Engineering and Computer Science, U-M Dearborn), Michael Cole (Michigan Medicine), and James Cooke (Michigan Medicine) - Combining ecological first principles and AI to better upscale and predict global carbon, nutrient and water cycles on a changing planet
Peter Reich (School for Environment and Sustainability) and Mohammed Ombadi (College of Engineering) - Machine Learning for Automated Fish Detection and Characterization
Katie Skinner (College of Engineering) and Jacob Allgeier (College of Literature, Science and the Arts) - Distributing Expert Attention in Complementary Systems
Sabina Tomkins (School of Information), Derek Van Berkel (School for Environment and Sustainability), Grant Schoenebeck (School of Information), Ariel Hasell (College of Literature, Science and the Arts), and John Ryan (College of Literature, Science and the Arts) - AI-driven Accelerated Optimization for the Design of Sustainable Aviation Fuels
Angela Violi (College of Engineering) - Neural Posterior Estimation (NPE) approaches for fitting high-dimensional stochastic epidemic models to real-world spatiotemporal disease data
Jon Zelner (School of Public Health) and Fan Bu (School of Public Health)
Track 2: Accelerating responsible AI research ecosystems
- A Joint Human-AI Framework for Responsible AI
Colleen Seifert (College of Literature, Science and the Arts), Rita Chin (College of Literature, Science and the Arts) and H.V. Jagadish (College of Engineering) - Advancing Responsible AI by Rethinking the Roles of Marginalized Communities in the Innovation Lifecycle: Developing the UBEC Approach
Shobita Parthasarathy (Ford School of Public Policy), Ben Green (School of Information) and Molly Kleinman (Ford School of Public Policy) - Innovating, Applying, and Educating on Fairness and Bias Methods for Educational Predictive Models
Christopher Brooks (School of Information), Libby Hemphill (Institute for Social Research and School of Information), and Allyson Flaster (Institute for Social Research) - Evaluating Solutions to the Decline of Online Knowledge Communities
Yan Chen (School of Information) and Qiaozhu Mei (School of Information)
Track 3: AI for Health Policy and Healthcare: Impact & Governance
- Trust, Governance, and Humans in the Loop in Clinical AI
Kayte Spector-Bagdady (Michigan Medicine) and Nicholson Price (Law School)
2023 PODS Awardees
- From ground to air, and the traveler experiences in-between: Human-centered data-driven performance measures for multimodal transportation systems
Atiyya Shaw (College of Engineering) and Max Li (College of Engineering) - A Data Science Toolkit for Examining Local Governance
Justine Zhang (School of Information) and Yanna Krupnikov (College of Literature, Science, and the Arts) - Bayesian modeling of multi-source phenology to forecast airborne allergen concentration
Kai Zhu (School for Environment and Sustainability) and Kerby Shedden (College of Literature, Science, and the Arts) - Interpretable machine learning to identify tumor spatial features from longitudinal multi-modality images for personalized progression risk prediction of poor prognosis head and neck cancer
Lise Wei (Michigan Medicine) and Liyue Shen (College of Engineering) - MI-SPACE: Multiplex Imaging based Spatial Analysis for Discovery of Cellular Interactions in the Tumor Microenvironment
Maria Masotti (School of Public Health) - Detecting and Countering Untrustworthy Artificial Intelligence (AI) through AI Literacy
Nikola Banovic (College of Engineering) - Foundations of Sequence Models for Learning, Estimation, and Control of Dynamical Systems
Samet Oymak (College of Engineering) and Necmiye Ozay (College of Engineering) - Neural Quantum States at Scale: Applications in Sciences and Engineering
Shravan Veerapaneni and James Stokes (College of Literature, Science, and the Arts) - Machine-Processing of Graduate Student Applications for Diversity, Equity, and Inclusion
Wenhao Sun (College of Engineering) and Dallas Card (School of Information)
2022 PODS Awardees
- A Machine-Learning Approach to Reduce Uncertainty in Climate Forcing by Aerosols
Joyce Penner (College of Engineering) - AI-based author entity disambiguation for promoting fair evaluation of women in science
Jinseok Kim (Institute for Social Research) - Building a genomic literature knowledgebase
Jie Liu (Michigan Medicine) - Combating and predicting drug resistance using a hybrid mechanistic machine learning model
Sriram Chandrasekaran (College of Engineering, Michigan Medicine) and Rudy Richardson (School of Public Health) - Developing a large-scale dataset to track romantic relationship formation and maintenance
Amie Gordon (College of Literature, Science, and the Arts) and Elizabeth Bruch (College of Literature, Science, and the Arts) - Developing Language-based Tools For Real-Time Counseling Feedback
Veronica Perez-Rosas (College of Engineering), Kenneth Resnicow (School of Public Health) and Rada Mihalcea (College of Engineering) - Improving Cardiovascular Disease Detection with a Novel Multi-label Classifier for Electrocardiograms: Capturing Label Uncertainty and Complex Hierarchical Relationships between Output Classes
Negar Farzaneh (Michigan Medicine) and Hamid Ghanbari (Michigan Medicine) - Machine Learning Guided Co-design for Reconstructive Spectroscopy
Qing Qu (College of Engineering) and Pei-Cheng Ku (College of Engineering) - Sustainability outcomes of restrictions on human actions: COVID-19 mobility changes, forest fires and air pollution across land regimes
Arun Agrawal (School for Environment and Sustainability), Ines Ibanez (School for Environment and Sustainability) and Yang Chen (College of Literature, Science, and the Arts) - Unlocking the vault: machine learning methods for the mobilization of data from millions of plant images
Stephen Smith (College of Literature, Science, and the Arts)
2021 PODS Awardees
- Images to Integrated Data: Piloting new methods to digitize, parse, and link historical records
J. Trent Alexander (Inter-university Consortium for Political and Social Research) and Sara Lafia (Inter-university Consortium for Political and Social Research) - Detecting Early-Warning Signals of Market Share Loss from Locus of Customer Movements
Syagnik Banerjee (School of Management, U-M Flint), Halil Bisgin (College of Innovation and Technology, U-M Flint) and Murali Mani (College of Innovation and Technology, U-M Flint) - Coordinated Multi-building Modeling and Management for Flexible Grid Service Innovation
Eunshin Byon (College of Engineering) and Raed Al Kontar (College of Engineering) - Exploring Attention-based Deep Learning Methods for Improving Students’ Ability to Engage with Scientific Literature
Kevyn Collins-Thompson (School of Information) and Yulia Sevryugina (U-M Library) - Robust Machine Learning under Distribution Shifts and Shocks: Application to Sustainable Air Quality
Paramveer Dhillon (School of Information) - IPODS: Innovative and Powerful Optimization Methods for Data Science with Statistical Guarantees
Salar Fattahi (College of Engineering) and Albert Berahas (College of Engineering) - Supporting Decision-making for a Vital Waterway in the Great Lakes by Machine Learning Model-based Lake Ice Forecasting
Ayumi Fujisaki-Manome (College of Engineering, Cooperative Institute for Great Lakes Research) and Christiane Jablonowski (College of Engineering) - Interpretable Machine Learning for Identifying Descriptors of Catalysts for Chemical Conversion
Bryan Goldsmith (College of Engineering) and Suljo Linic (College of Engineering) - Ensuring FAIRness in Social Media Archives
Libby Hemphill (Inter-university Consortium for Political and Social Research) - Measuring Racial Disparity in the Language of Physician-Patient Interactions
David Jurgens (School of Information) and Allison Earl (College of Literature, Science, and the Arts) - Equitable Models for Persistent Opioid Use Prediction and Personalization
Rahul Ladhania (School of Public Health) and Anne Fernandez (Michigan Medicine) - Discovering Causes of Cancer Recurrence Through Inverse Reinforcement Learning
Gary Luker (Michigan Medicine), Jennifer Linderman (College of Engineering) and Kathryn Luker (Michigan Medicine) - Using Geospatial Data Science to Identify Vulnerable Communities to Climate Change
Joshua Newell (School for Environment and Sustainability), Marie O’Neill (Environmental Health Sciences) and Carina Gronlund (Survey Research Center) - Machine Learning Augmented System for Continuous Fetal Monitoring
Kathleen Sienko (College of Engineering), Carrie Bell (Michigan Medicine) and Noel Perkins (College of Engineering) - Classifying the Content of Undergraduate Course-taking at Scale
Kevin Stange (Ford School of Public Policy), Allyson Flaster (Inter-university Consortium for Political and Social Research) - Data Science Approach towards a Socio-ecological Framework for the Investigation of Continental Urban Stream Water Quality Pattern
Runzi Wang (School for Environment and Sustainability), Yang Chen (College of Literature, Science, and the Arts) and William S. Currie (School for Environment and Sustainability) - Scientifically-Structured Latent Variable Methods for High-Dimensional Data to Individualize Healthcare
Zhenke Wu (School of Public Health)